Algorithms and Creating Community
Overview Whether you realize it or not, you have come in contact with software created solely for the purpose of learning more about you as a consumer. They take many forms, the recommended shows on Netflix, the "other videos you may like" on YouTube, the suggested artists on Spotify. But all of these things have one thing in common, all are algorithm based. Before I get deeper into this, an algorithm is a logical arithmetical procedure that if correctly applied, ensures the solution of a problem. But what is the problem to be solved in the case of algorithms directed towards media? And how does the solution of these problems aid in creating communities? This page aims to give answers to these questions. The Problem of the Media Algorithm: Big Business Side Before this delves into the more thought provoking part of the article, first look at why these algorithms exist in the first place. To draw an example: You've been awake all night binge watching the final seasons of "How I Met Your Mother"', the last show on your 'to watch' list, when you finally reach your goal of completing the series. Your focus on viewing all nine seasons has left you with a question as to what to watch next. This is the problem big business aims to solve. If you knew not of any other shows worth watching, getting off Netflix to do something else just became that much easier. But a light shines in the darkness of your contemplation, the recommended videos. These videos have been selected for you based on your viewing preferences and that of those who have these similar viewing habits for the purpose of increasing your use. If an algorithm were used on a shopping sight, you would see products more tailored to what you're expected to like, and by that logic, are more likely to frequent the site to do your shopping. But What Does THAT ''Have To Do With Creating Communities? What a wonderful question, see, when somebody clicks that one song on YouTube (ever more so if you liked the video), their choice was recorded and that information was plugged into an algorithm. The recommendations received thereafter represent what the algorithm was lead to think they'd be interested in. But after so many clicks, one can only receive so many recommendations, and these are hardly unique. When viewing "Lose Yourself" by Eminem on YouTube, the videos to the right of the video window will be widely similar from person to person. What this does is increase awareness of specific media, in this case, "Till I Collapse" which appears almost infallibly. It is unlikely a song by lesser known artists such as Aesop Rock after searching for music by A$AP Rocky to be suggested, and vice versa. This tends to create an ingroup and outgroup with music through the boundary generated in the software, whereas people who frequently listen to lesser known artists versus big names are profiled by their music choices and less likely to come in contact with the big name music, often separating the two through musical preference. As you would expect, this generates community in places such as the comment or review sections, in most cases people will be more like minded and agreeing of , let's say, how great the movie "Gravity" was if they've been recommended it through their pattern of watching space related media. Inversely, a history buff may find such a movie out of their taste, and instead their recommendations are littered with documentaries. It is unlikely these two will cross paths on the internet due to the boundary separating them which an algorithm has put in place, though they will likely come across other space/history buffs respectively and though they may have differed views, they will likely have similar tastes in media. 'To Put It In Other Words' Algorithms are a complex idea to explain, and as they are not physically manifested, they are equally as difficult to demonstrate, but below you will see a TED talk that may be able to help explain some things more clearly. Sources Beer, David. "Chapter 4. Algorithms: Shaping Tastes and Manipulating the Circulations of Popular Culture pages 63–100" ''Popular Culture and New Media the Politics of Circulation''. New York: Palgrave Macmillan, 2013. 63-100'' Jenkins, Henry. "If It Doesn’t Spread, It’s Dead (Part Six): Spreadable Content." Confessions of an AcaFan. 23 Feb. 2009. Web. 13 Oct. 2015. Attali, Jacques. "Chapter One: Listening." Noise: The Political Economy of Music''. Minneapolis: U of Minnesota, 1985. 3-20. Print. '' Ray, Brian. "More Than Just Remixing: Uptake and New Media Composition." Www.sciencedirect.com. Elsevier, 2013. Web. 14 Oct. 2015. Category:Additions Encouraged